ENGG 390 Reports (MEM Students)
Degree Program
M.E.M
Year of Graduation
2019
Sponsor
Equilend, New York, NY
Faculty Advisor
Geoff G. Parker
Document Type
Report
Publication Date
Fall 9-12-2018
Abstract
Clients using the Equilend platform for post trade monitoring often experience “trade breaks” produced when small shifts in the market cause the once agreed upon terms of a trade to fall out of balance. In order to accelerate the process of fixing trade breaks, we examined historical user data and used machine learning algorithms from open source libraries to predict how those same users would fix their current and future breaks. Deliverables included background research; a clean, robust data pipeline; and a proof of concept machine learning framework. The highest performing algorithm was 98.67% accurate at predicting client break solutions.
Level of Access
Restricted: Campus/Dartmouth Community Only Access
Dartmouth Digital Commons Citation
Bertsch, Spencer, "Implementing AI In Post Trade Suite" (2018). ENGG 390 Reports (MEM Students). 25.
https://digitalcommons.dartmouth.edu/engs390/25
Restricted
Available to Dartmouth community via local IP address.